Plotly

• Plotly is an interactive data visualization library that allows you to create web-based, interactive visualizations and dashboards. It supports a wide range of chart types and is often used for creating dynamic and interactive plots.

Modules in Plotly

1. plotly.graph_objects : It module contains the objects (Figure, layout, data, and the definition of the plots like scatter plot, line chart) that are responsible for creating the plots.
2. plotly.express : plotly.express module can create the entire Figure at once. It uses the graph_objects internally and returns the graph_objects.Figure instance.

• Note: In summary, if you need fine-grained control and customization, or if you are creating complex visualizations, plotly.graph_objects might be more suitable. If you want a quick and easy way to create common plots with less code, plotly.express is a convenient choice

pip install plotly

Requirement already satisfied: plotly in c:\users\asus\anaconda3\lib\site-packages (5.9.0)
Requirement already satisfied: tenacity>=6.2.0 in c:\users\asus\anaconda3\lib\site-packages (from plotly) (8.0.1)
Note: you may need to restart the kernel to use updated packages.

#plotly.graph_objects
import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1,2,3,4,5],y=[3,5,6,7,8],mode='markers'))

fig = go.Figure()
fig.add_trace(go.Scatter(x=[1,2,3,4,5],y=[3,5,6,7,8],mode='lines'))

fig = go.Figure()
fig.add_trace(go.Bar(x=[1,2,3,4,5],y=[3,5,6,7,8]))
fig.show()

x1 = [1,2,2,2,3,3,4,1,5,6,8,1,9,10,12,13]
fig = go.Figure(data=[go.Histogram(x=x1)])
fig.show()

x1

[1, 2, 2, 2, 3, 3, 4, 1, 5, 6, 8, 1, 9, 10, 12, 13]

x = [1,2,3,3,4,4,5,5,6,6,7,78,9,9,0,8,9,8,7,6,5,1,2,2,23,34,45,5]
fig = go.Figure(data=[go.Histogram(x=x)])
fig.show()

import seaborn as sns

df = sns.load_dataset('tips')

df

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

fig= go.Figure(data=[go.Histogram(x=df.total_bill)])
fig.show()

fig = go.Figure()
fig.add_trace(go.Scatter(x=df.total_bill,y=df.tip,mode='markers'))

fig = go.Figure()
fig.add_trace(go.Scatter3d(x=[1,2,3,4,5],y=[3,5,6,7,8],mode='markers',z=[2,3,4,5,6]))
fig.show()

#plotly.express

import plotly.express as px

fig = px.line(x=[1,2,3],y=[1,2,3])
fig.show()

df = px.data.iris()

df

	sepal_length	sepal_width	petal_length	petal_width	species	species_id
0	5.1	3.5	1.4	0.2	setosa	1
1	4.9	3.0	1.4	0.2	setosa	1
2	4.7	3.2	1.3	0.2	setosa	1
3	4.6	3.1	1.5	0.2	setosa	1
4	5.0	3.6	1.4	0.2	setosa	1
...	...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	virginica	3
146	6.3	2.5	5.0	1.9	virginica	3
147	6.5	3.0	5.2	2.0	virginica	3
148	6.2	3.4	5.4	2.3	virginica	3
149	5.9	3.0	5.1	1.8	virginica	3

150 rows × 6 columns

fig=px.line(df , x='species',y='petal_width')
fig.show()

fig=px.bar(df , x='sepal_length',y='petal_width')
fig.show()

fig=px.histogram(df , x='sepal_length',y='petal_width')
fig.show()

fig=px.scatter(df , x='sepal_length',y='petal_width')
fig.show()

fig=px.scatter(df , x='species',y='petal_width',size='petal_length',color='species')
fig.show()

df = px.data.tips()

df

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

fig=px.pie(df , values='total_bill', names='day')
fig.show()

fig=px.box(df , x='day', y='total_bill')
fig.show()

fig=px.violin(df , x='day', y='total_bill')
fig.show()